A facial recognition technology is to recognize a face image from an image shot by a video camera. When the video camera is shooting a face, the face performs a head motion such as head raising, head lowering, rotating to left or rotating to right. As a result, an angle exists between a face in an image shot by the video camera and a face of a frontal face image, the angle is a facial pose angle, and the facial recognition technology needs to determine the facial pose angle in the image, and can recognize a face image from the image according to the facial pose angle.
Currently, a facial pose angle is determined by using the following method: a face is made to perform head motions in different rotation directions in advance, face images in the different rotation directions are shot by using a video camera, texture features of a face image in each rotation direction is analyzed separately, and each rotation direction is made to correspond to the texture features of the face image in each rotation direction to form a correspondence. When a facial pose angle of a face image needs to be determined, texture features of the face image is analyzed, a correspondence is searched for texture features that are most similar to the texture features of the face images for which facial pose is being determined, a facial pose direction corresponding to the most similar texture features is obtained, and the facial pose angle of the face image is estimated according to the facial pose direction and the texture features.
The conventional method for determining a facial pose angle based on texture features, only a rough angle of facial pose can be determined, while a specific facial pose angle cannot be determined. Moreover, texture feature analysis is a complex process, and it is prone to incorrect facial pose angle because texture features were analyzed inaccurately.
There are many applications for facial pose angle determination, such as virtual reality and augmented reality applications, accessibility applications, image selection and recommendations, information presentation based on user's focus, etc. In these applications, the accuracy of facial pose angle determination and the speed of the determination are very important to the utility of the applications and the user experiences. In addition, in a lot of applications, the device that perform the determination are small, portable devices with limited processing power and battery life, thus, it is more important that the facial pose angle determination is fast, efficient, and less computation and memory intensity as those required by the conventional facial pose determination methods.